Justin Greis

📌 A very cool milestone on our journey to build something special!

🙏 Huge thanks to Scott Slavick at Barack Ferrazzano Kirschbaum & Nagelberg LLP for his guidance and support along the way.

Appreciate Evan Schuman featuring my perspective in this discussion on one of the biggest challenges facing enterprise AI adoption: pricing.

As organizations move from experimentation to enterprise-scale deployment, the conversation is shifting beyond model performance and toward a more fundamental question: how should AI be valued?

My view is that much of the market is still trying to fit AI into traditional infrastructure pricing models, when AI is increasingly being used to augment work, transform processes, and create business outcomes.

The opportunity ahead isn’t simply finding better pricing mechanisms. It’s creating stronger alignment between technology investments, business objectives, and measurable value.

As AI capabilities continue to mature, I expect pricing models, governance approaches, and value realization frameworks to evolve alongside them.

And in the meantime, token prices are climbing higher and higher! To be expected as adoption grows…

Worth a read for leaders thinking through the next phase of AI adoption.

📈 AI pricing remains one of the most important conversations facing enterprise leaders today.

As AI evolves from a technology tool into a business capability, organizations are rethinking how they evaluate value, investment, and outcomes.

Three key takeaways from this discussion:

🏆 AI success should be measured by business impact, not technical consumption metrics alone.

💰 Pricing models will continue to evolve as organizations gain greater clarity on how AI creates value across the enterprise.

🔭 Strong governance, clear objectives, and executive accountability remain the foundation of successful AI adoption.

Appreciate Evan Schuman for including our perspective in this timely conversation about the future of enterprise AI. Check out his latest ComputerWorld article titled, "The AI pricing conundrum - it started as a nightmare, now it’s worse."

👇 Link to the full article in the comments below.

🏎️ AI adoption is moving fast...and that’s a good thing!

The question I keep coming back to is simple: can we scale this without breaking the economics, especially once it’s embedded in price-sensitive products?

Pilots are easy to love, but PRODUCTION is where the math gets real.

Architecture, model choice, token usage, vendor terms, and workflow design all start to matter once AI is running inside the business every day and embedded into its DNA.

I don’t think the answer is to slow down. I think the answer is to scale smarter.

acceligence Partner and CIO, Yuri Goryunov, shares an excellent perspective on why AI cost discipline will become a bigger leadership topic in 2027, and what executives can do now to get ahead of it.

Curious how others are thinking about this: are you already modeling the cost side of AI at scale?

👇 Read the full article below or at acceligence.com: https://acceligence.com/insights/2026/05/04/scaling-ai-without-breaking-the-economics/

💡 acceligence insights: Scaling AI without breaking the economics

AI adoption is entering its next phase: not less ambition, but better economics. Leaders who pair speed with cost discipline now will be positioned to scale AI confidently, protect margins, and create lasting advantage.

🔗 Read the full article here: https://hubs.la/Q04fdfff0